urn:lsid:ibm.com:blogs:entries-a27fff3d-0d1b-4145-b91e-6726fb6cadf1Insurance - Tags - deep_analytics Incorporate new approaches to address uncertainty and complexity.12013-06-26T15:55:07-04:00IBM Connections - Blogsurn:lsid:ibm.com:blogs:entry-ba2f47c3-ad8b-4784-a026-d82689fe9824Big Data: Why should we even care?Jamie BiskerJBISKER@US.IBM.COM120000DW9GactiveComment Entriesapplication/atom+xml;type=entryLikes2012-03-05T04:58:21-05:002012-03-05T05:12:41-05:00<p>The topic of big data has come up many times in the course of my recent travels to meet with business and technology executives in the insurance industry. Sometimes these industry players are interested in what their peers are up to, some seem to have a clear mandate to handle some portion of it, and others are curious as to whether this characterization of the modern world is anything more than a marketing push by IT vendors. Ironically, big data can’t be said to be at the core of the issue so much as it is the new wrapper around an existing situation. And it is a wrapper with more than a few layers.</p>
<p><span style="font-weight: bold;">A Definition</span><br>Before we proceed, a definition is in order. I will create my own version of what ‘big data’ entails from my readings and experience:</p>
<div style="margin-left: 40px;"><span style="font-style: italic;">Big data is the nascent collection and storage of information from either existing or novel sources in combinations of such detail, depth, or at a sampling rate as to create files, databases, file systems, etcetera of exceptionally large size.</span><br></div><br>
<p>Early examples of big data could include the transaction data the Wal*Mart collected in its data warehouse for all its sales inventory and transactions which exceeded 10TB in the early 1990s.&nbsp; A brief sampling of prototypical examples today include:<br></p><ul><li>Cellular Telephony – voice, texting, and data traffic have created larger data stores not to mention the complex billing records needed to charge for these services;</li><li>Radio Frequency ID tags – Supply chain management, security systems for retail and wholesale inventories, transportation tracking for toll collection and homeland security contribute large numbers of transaction that must be tallied;</li><li>Satellite data – Global positioning data, remote sensing (think weather &amp; geographic imaging), communications and space telescopes all send down many terabytes of data per day. Some of this data is used in the insurance industry for risk management purposes;</li><li>Usage-based data for insurance pricing and underwriting – collecting real-time or batched information from moving vehicles at a rate needed to be useful can create petabytes of data which in turn may need to be expanded to be made useful for pricing, underwriting, and tracking purposes.</li></ul><br><p>
</p><p><span style="font-weight: bold;">Out From the Past</span><br>Like last week’s analysis of why financial services providers don’t ‘get’ social media, some carriers are hesitant to acknowledge the value, if not the impact of what big data means to the industry. It used to be that insurers could rely on the mastery of a few chosen, timeworn industry metrics to succeed. Pick one or two, and nail down the processes that effect them, and then market yourself around lower cost, great service, or innovative products. The primary data required was in policy or claims administration system files, and a carrier would access external data as required from other industry sources. This created large files, but did not really cross into the big data sphere unless aggregated across many companies.
</p><p><span style="font-weight: bold;">A Processing Core</span><br>All this still works of course, but such approaches will increasingly be deemed provincial as they fail to stand up to those companies that understand and exploit the value and knowledge within big data. One way to think about the productive use of and transition to big data for insurers is in terms of the aforementioned layers.<br><br>The information that in the past was sufficient to run an insurance operation can be considered the core of today’s multilayered data diagram. Even getting that data to provide its potential value requires master data management and other methodologies that enforce consistency and quality. And this description of legacy data architecture really has the processing power at its center, with core systems data ringing it. The next ring out is now where large data sets reside – the data warehouses, external databases, and perhaps some growing big data stores.
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<span style="font-weight: bold;">Layers of Success</span><br>The model of analyzing historical data tempered with some current information to connect the two elements will give way to more real time criteria from big data sources. Going forward, successful insurers will invert their processing and data layers to put relevant data at the core. The processing power will then ring it acting as a filtering mechanism to deal with the onslaught of big data. The ratio of historical core data to just-in-time information and knowledge will go from 90/10 to ranges more like 50/50, or eventually 70/30 or higher.<p>
</p><p><span style="font-weight: bold;">I’m Thinking</span><br>it will increasingly be the mastery and inclusion of big data elements that will support greater chance of success. Data isn’t simply for retroactive analysis and batch processing even if business intelligence or predictive analytics tools exist to uncover greater levels of information and knowledge. Carriers that can effectively incorporate the data from RFID devices, Twitter feeds, and weather satellites into core processes are the future of our industry.</p>
<p>A final thought came to me upon rereading last week’s posting about the uptake of social media by the financial services industry. My insight comes from working on an internal study of social media use in the insurance industry and how it’s connected to big data. I contributed a couple of data charts about popular social sites and how groups of carriers ranked against each other in their use of those sites. The thing that we had to stress to our customers when presenting this work was that while action in the social media space is of course optional, the cost of inaction only rises with delay – it (social media) is happening all around you regardless of your stance. Social media is just one form of big data that can impact insurers, and to be sure, others are happening all around insurers as well. </p>
<p>Can your company adapt?</p>
The topic of big data has come up many times in the course of my recent travels to meet with business and technology executives in the insurance industry. Sometimes these industry players are interested in what their peers are up to, some seem to have a clear...012039urn:lsid:ibm.com:blogs:entries-a27fff3d-0d1b-4145-b91e-6726fb6cadf1Insurance2013-06-26T15:55:07-04:00